A quantitative structure- activity relationship (QSAR) has been widely used to investigation a correlation between chemical structures of molecules to their activities. In the present study, QSAR models have been carried out on 76 camptothecin (CPT) derivatives as anticancer drugs to determine the 14N nucleus quadrupole coupling constants (QCC). These quantum chemical properties have been calculated using Density Functional Theory (DFT) and B3LYP/6-311G (d, p) method in the gas phase. A training set of 60 CPT derivatives were used to construct QSAR models and a test set of 16 compounds were used to evaluate the build models that were made using multiple linear regression (MLR) analysis. Molecular descriptors were calculated by Dragon software, and the stepwise multiple linear regression and the Genetic algorithm (GA) techniques were used to select the best descriptors and build QSAR models respectively. QSAR models were used to delineate the important descriptors responsible for the properties of the CPT derivatives. The statistically significant QSAR models derived by GA-MLR analysis were validated by Leave-One-Out Cross-Validation (LOOCV) and external validation methods. The multicollinearity of the descriptors contributed in the models was tested by calculating the variance inflation factor (VIF) and the DurbinWatson (DW) statistics. The predictive ability of the models was found to be satisfactory. The results of QSAR study show that quantum parameters, 2D autocorrelations and Walk and path counts descriptors contains important structural information sufficient to develop useful predictive models for the studied activities. Manuscript profile

Abstract. Topological indices are the numerical value associated with chemical constitution purporting for correlation of chemical structure with various physical properties, chemical reactivity or biological activity. Graph theory is a delightful playground for the exploration of proof techniques in Discrete Mathematics and its results have applications in many areas of sciences. A graph is a topological concept rather than a geometrical concept of fixed geometry, and hence Euclidean metric lengths, angles and three-dimensional spatial configurations have no meaning. One of the useful indices for examination of structure- property relationship is Randic' index. In this study, the relationship between the Randic'(1X), Balaban (J) and Szeged (Sz) indices and Harary numbers (H) to the thermal energy (Eth), heat capacity (Cv) and entropy(S) of monocarboxylic acids (C2- C20) are established. The thermodynamic properties are taken from HF level using the ab initio 6-31 G basis sets from the program package Gussian 98. Then, some useful topological indices for examination of the structure- property relationship are presented. Manuscript profile

Quantitative structureproperty relationship (QSPR) models establish relationships between different types of structural information to their properties. In the present study the relationship between the molecular descriptors and quantum properties consist of the heat capacity (Cv/J mol-1K-1) entropy (S/J mol-1K-1) and thermal energy (Eth/kJ mol-1) of 100 alkenes is represented. Genetic algorithm (GA) and backward-multiple linear regressions (BW-MLR) were successfully developed to predict quantum properties of alkenes. Molecular descriptors were calculated with Dragon software and the genetic algorithm (GA) method was used to selected important molecular descriptors. The quantum properties were obtained from quantum-chemistry technique at the Hartree-Fock (HF) level using the ab initio 6-31G* basis sets. The predictive powers of the BW-MLR models were discussed by using leave-one-out (LOO) cross-validation and external test set. Results showed that the predictive ability of the models was satisfactory, and the 2D matrix-based descriptors, topological, edge adjacency and Connectivity indices could be used to predict the mentioned properties of 100 alkenes Manuscript profile

Imidazole is compound with a wide range of biological activities and imidazole derivatives are the basis of several groups of drugs.In this study the relationship between molecular descriptors and the thermal energy (Eth kJ/mol), and heat capacity (Cv J/mol) of imidazole derivatives is studied. The chemical structures of 85 Imidazole derivatives were optimized at HF/6-311G* level with Gaussian 98 software.Molecular descriptors were calculated for selected compound by using the Dragon software.The Genetic algorithm- multiple linear regression (GA-MLR) and backward methods were used to select the suitable descriptors and also for predicting the thermodynamic properties of imidazole derivatives.The obtained models were evaluated by statistical parameters, such as correlation coefficient (R2adj), Fisher ratio (F), Root Mean Square Error (RMSE), Durbin-Watson statistic (D) and significance (Sig).The predictive powers of the GA- MLR models are studied using leave-one-out (LOO) cross-validation and external test set. The predictive ability of the GA-MLR models with two-three selected molecular descriptors was found to be satisfactory. The developed QSPR models can be used to predict the property of compounds not yet synthesized. Manuscript profile

A QSPR study on a series of 2-Phenylindole derivatives as anticancer agents was performed to explore the important molecular descriptor which is responsible for their thermodynamic properties such as heat capacity (Cv) and entropy(S).Molecular descriptors were calculated using DRAGON software and the Genetic Algorithm (GA) and backward selection procedure were used to reduce and select the suitable descriptors. Multiple Linear Regression (MLR) analysis was carried out to derive QSPR models, which were further evaluated for statistical significance such as squared correlation coefficient (R2) root mean square error (RMSE), adjusted correlation coefficient (R2adj) and fisher index of quality (F).The multicollinearity of the descriptors selected in the models were tested by calculating the variance inflation factor (VIF), Pearson correlation coefficient (PCC) and the DurbinWatson (DW) statistics. The predictive powers of the MLR models were discussed using Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The best QSPR models for prediction the Cv(J/molK) and S(J/molK), having squared correlation coefficient R2 =0.907 and 0.901, root mean squared error RMSE=2.019 and RMSE= 2.505, and cross-validated squared correlation coefficient R2 cv = 0.902 and 0.889, respectively. The statistical outcomes derived from the present study demonstrate good predictability and may be useful in the design of new 2-Phenylindole derivatives. Manuscript profile